Cyber-Physical Co-Design Reliability Framework for ASIL-D Automotive Sensor ECUs with Integrated Hardware–Software Fault Tolerance and Security
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The extended complexity of the electronics control units (ECUs) of autonomous and electric cars makes it necessary to implement fault-tolerant designs that comply with the ISO26262ASIL-D. The paper will discuss how hardware-software co-design is used in guaranteeing the safety and reliability of automotive sensor ECUs. The systematic review of 21 articles published between 2021 and 2025 lists integrated strategies related to redundancy, virtualisation, artificial intelligence, and cybersecurity to attain the fail-operational resilience. In the research, the co-designed systems have been shown to have a 90 per cent diagnostic coverage, less than 5 ms recovery latency, and 95 per cent fault detection performance, which is much better than the traditional modular design. Hardware redundancy ensures physical resilience, and adaptive software enables the tasks and proactive fault recovery to be transmitted without difficulties. Moreover, there are cybersecurity features, including voltage-based ECU fingerprinting and root-of-trust verification, to improve the reliability of communications. This paper suggests the Co-Design Reliability Enhancement Framework (CREF) that has the capability of guaranteeing compliance with ASIL-D through the incorporation of redundancy, artificial intelligence, and fault prediction, as well as pipeline testing. The framework illustrates that cybersecurity and functional safety will need to go together, and the ideas of co-design underlie the design of the next-generation, software-defined, fault-tolerant vehicles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it